./episode_142.sh

📈 An investment in knowledge pays the best interest.

Aug 20, 2023

Hey friends & nerds! 👋

Welcome to the Sunday Science Newsletter where we explore science, systems & tools that help us become smarter scientists.

Work with me 🤝

🎙️ How Amazon Made Billions and Paid Sepp With a Drink….

💻 Ansys Notebook

Cloud based programming experience for simulation

Access to Ansys Simulation Technologies
Intuitive cell-driven, browser-native environment
Built on the OnScale Platform
Consistent User Experience

🎬 My Favourite Video on CFD Training

🧠 Learning governing physics from output only measurements

Extracting governing physics from data is a key challenge in many areas of science and technology. The existing techniques for equations discovery are dependent on both input and state measurements; however, in practice, we only have access to the output measurements only. We here propose a novel framework for learning governing physics of dynamical system from output only measurements; this essentially transfers the physics discovery problem from the deterministic to the stochastic domain.

The results obtained indicate the potential of the proposed approach in identifying governing physics from output only measurement.

Read Paper

🧠 Powerful ‘Machine Scientists’ Distill the Laws of Physics From Raw Data

Astrophysicists modeled the solar system’s behavior in two ways. First, they used decades of NASA data to train a neural network. They then used a symbolic regression algorithm to further distill that model into an equation. In these videos — which show true positions as solid objects, and model predictions as wire mesh outlines — the neural network (left) does far worse than the symbolic regression algorithm (right).

Full Article

🧠 Connected Papers – Explore Connected Papers in a Visual Graph

Get a visual overview of a new academic field

Enter a typical paper and we'll build you a graph of similar papers in the field. Explore and build more graphs for interesting papers that you find - soon you'll have a real, visual understanding of the trends, popular works and dynamics of the field you're interested in.

Make sure you haven't missed an important paper

In some fields like Machine Learning, so many new papers are published it's hard to keep track. With Connected Papers you can just search and visually discover important recent papers. No need to keep lists.

Create the bibliography for your thesis

Start with the references that you will definitely want in your bibliography and use Connected Papers to fill in the gaps and find the rest!

Discover the most relevant prior and derivative works

Use our Prior Works view to find important ancestor works in your field of interest. Use our Derivative Works view to find literature reviews of the field, as well as recently published State of the Art that followed your input paper.

Website.

💻 Engineering Tool of the Week – Semtex

Semtex is a 'classical' quadrilateral spectral element incompressible direct numerical simulation code that uses the standard nodal GLL basis functions to provide two-dimensional solutions and (optionally) Fourier expansions in a homogeneous direction to provide three-dimensional solutions. If your fluid mechanics problem is two-dimensional or has a natural periodic coordinate direction in 3D (a cylindrical coordinate system is perhaps the most obvious example), semtex could be a good choice.

Official Website

📚Book of the Week

Fluid Simulation for Computer Graphics

Animating fluids like water, smoke, and fire using physics-based simulation is increasingly important in visual effects, in particular in movies, like The Day After Tomorrow, and in computer games. This book provides a practical introduction to fluid simulation for graphics. The focus is on animating fully three-dimensional incompressible flow, from understanding the math and the algorithms to the actual implementation.

Get on Amazon